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ADOL-C: Computing higher-order derivatives and sparsity pattern for functions written in C/C++
Conference ObjectAbstract: This paper presents ADOL-C, a software package for the Automatic Differentiation of C and C++ codes.Palabras claves:Automatic differentiation, Forward mode, Higher-order derivatives, Reverse mode, Sparsity detectionAutores:Andreas Griewank, Walther A.Fuentes:scopusAchieving logarithmic growth of temporal and spatial complexity in reverse automatic differentiation
ArticleAbstract: In its basic form the reverse mode of automatic differentiation yields gradient vectors at a small mPalabras claves:Adjoint, Checkpointing, Complexity, Gradient, RecursionAutores:Andreas GriewankFuentes:scopusAlgorithm 799: Revolve: An implementation of checkpointing for the reverse or adjoint mode of computational differentiation
ArticleAbstract: In its basic form, the reverse mode of computational differentiation yields the gradient of a scalarPalabras claves:Adjoint mode, ALGORITHMS, Checkpointing, Computational differentiation, Reverse modeAutores:Andreas Griewank, Walther A.Fuentes:scopusDerivative Convergence for Iterative Equation Solvers
ArticleAbstract: When nonlinear equation solvers are applied to parameter-dependent problems, their iterates can be iPalabras claves:Automatic differentiation, Derivative convergence, Implicit functions, Newton-like methods, preconditioning, Secant updatesAutores:Andreas Griewank, Bischof C., Carle A., Corliss G.F., Williamson K.Fuentes:scopusFrom the product example to PDE adjoints, algorithmic differentiation and its application (Invited talk)
Conference ObjectAbstract: At the last conference on algorithmic or automatic differentiation (AD) in July 2012, Bert SpeelpennPalabras claves:Autores:Andreas GriewankFuentes:scopusOn automatic differentiation and algorithmic linearization
ArticleAbstract: We review the methods and applications of automatic differentiation, a research and development actiPalabras claves:Jacobians, Piecewise linearization, Taylor expansionsAutores:Andreas GriewankFuentes:scopusOn stable piecewise linearization and generalized algorithmic differentiation
ArticleAbstract: It is shown how functions that are defined by evaluation programs involving the absolute value functPalabras claves:ADOL-C, Automatic differentiation, Bouligand derivative, bundle methods, coherent orientation, computational graph, conical activity, directional derivative, generalized gradients and Jacobians, Lipschitz continuity, midpoint method, piecewise differentiability, piecewise Newton, trapezoidal ruleAutores:Andreas GriewankFuentes:scopusOn the numerical stability of algorithmic differentiation
Conference ObjectAbstract: In contrast to integration, the differentiation of a function is an ill-conditioned process, if onlyPalabras claves:Automatic differentiation, Forward-backward stability, IEEE arithmetic, Rounding error estimates, Ultimate roundingAutores:Andreas Griewank, Kulshreshtha K., Walther A.Fuentes:scopusIndex determination in DAEs using the library indexdet and the ADOL-C package for algorithmic differentiation
Conference ObjectAbstract: We deal with differential algebraic equations (DAEs) with properly stated leading terms. The calculaPalabras claves:ADOL-C, Differential algebraic equations, Tractability indexAutores:Andreas Griewank, Lamour R., Monett D.Fuentes:scopusPiecewise linear secant approximation via algorithmic piecewise differentiation
ArticleAbstract: It is shown how piecewise differentiable functions F:ℝ n ↦ℝ m that are defined by evaluation programPalabras claves:49J52, 65D25, 65K10, ADOL-C, Automatic differentiation, generalized hermite interpolation, generalized Newton's method, Lipschitz continuity, stable piecewise linearizationAutores:Andreas Griewank, Hasenfelder R., Lehmann L., Radons M., Streubel T.Fuentes:scopus